Used Cars Prediction Analysis¶
There are numerous aspects to take into consideration while purchasing a car – the main being should you buy a new or a used car. If you are trying to manage your finances wisely, opting for a pre-owned car would be a wise decision. Though the idea of purchasing a new car may sound tempting, the quick rate of depreciation, higher price, and greater insurance, among others, do not work in the favor of new cars.
Importing necessary libraries¶
Data Preprocessing¶
| Unnamed: 0 | Name | Location | Year | Kilometers_Driven | Fuel_Type | Transmission | Owner_Type | Mileage | Engine | Power | Seats | New_Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Maruti Alto K10 LXI CNG | Delhi | 2014 | 40929 | CNG | Manual | First | 32.26 km/kg | 998 CC | 58.2 bhp | 4.0 | NaN |
| 1 | 1 | Maruti Alto 800 2016-2019 LXI | Coimbatore | 2013 | 54493 | Petrol | Manual | Second | 24.7 kmpl | 796 CC | 47.3 bhp | 5.0 | NaN |
| 2 | 2 | Toyota Innova Crysta Touring Sport 2.4 MT | Mumbai | 2017 | 34000 | Diesel | Manual | First | 13.68 kmpl | 2393 CC | 147.8 bhp | 7.0 | 25.27 Lakh |
| 3 | 3 | Toyota Etios Liva GD | Hyderabad | 2012 | 139000 | Diesel | Manual | First | 23.59 kmpl | 1364 CC | null bhp | 5.0 | NaN |
| 4 | 4 | Hyundai i20 Magna | Mumbai | 2014 | 29000 | Petrol | Manual | First | 18.5 kmpl | 1197 CC | 82.85 bhp | 5.0 | NaN |
| Unnamed: 0 | Name | Location | Year | Kilometers_Driven | Fuel_Type | Transmission | Owner_Type | Mileage | Engine | Power | Seats | New_Price | Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Maruti Wagon R LXI CNG | Mumbai | 2010 | 72000 | CNG | Manual | First | 26.6 km/kg | 998 CC | 58.16 bhp | 5.0 | NaN | 1.75 |
| 1 | 1 | Hyundai Creta 1.6 CRDi SX Option | Pune | 2015 | 41000 | Diesel | Manual | First | 19.67 kmpl | 1582 CC | 126.2 bhp | 5.0 | NaN | 12.50 |
| 2 | 2 | Honda Jazz V | Chennai | 2011 | 46000 | Petrol | Manual | First | 18.2 kmpl | 1199 CC | 88.7 bhp | 5.0 | 8.61 Lakh | 4.50 |
| 3 | 3 | Maruti Ertiga VDI | Chennai | 2012 | 87000 | Diesel | Manual | First | 20.77 kmpl | 1248 CC | 88.76 bhp | 7.0 | NaN | 6.00 |
| 4 | 4 | Audi A4 New 2.0 TDI Multitronic | Coimbatore | 2013 | 40670 | Diesel | Automatic | Second | 15.2 kmpl | 1968 CC | 140.8 bhp | 5.0 | NaN | 17.74 |
Unnamed: 0 int64 Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage object Engine object Power object Seats float64 New_Price object Price float64 dtype: object
Unnamed: 0 int64 Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage object Engine object Power object Seats float64 New_Price object dtype: object
Rows in dataset are : 6019 Columns in dataset are : 14
Rows in test dataset are : 1234 Columns in test dataset are : 13
| Unnamed: 0 | Year | Kilometers_Driven | Seats | Price | |
|---|---|---|---|---|---|
| count | 6019.000000 | 6019.000000 | 6.019000e+03 | 5977.000000 | 6019.000000 |
| mean | 3009.000000 | 2013.358199 | 5.873838e+04 | 5.278735 | 9.479468 |
| std | 1737.679967 | 3.269742 | 9.126884e+04 | 0.808840 | 11.187917 |
| min | 0.000000 | 1998.000000 | 1.710000e+02 | 0.000000 | 0.440000 |
| 25% | 1504.500000 | 2011.000000 | 3.400000e+04 | 5.000000 | 3.500000 |
| 50% | 3009.000000 | 2014.000000 | 5.300000e+04 | 5.000000 | 5.640000 |
| 75% | 4513.500000 | 2016.000000 | 7.300000e+04 | 5.000000 | 9.950000 |
| max | 6018.000000 | 2019.000000 | 6.500000e+06 | 10.000000 | 160.000000 |
| Unnamed: 0 | Year | Kilometers_Driven | Seats | |
|---|---|---|---|---|
| count | 1234.000000 | 1234.000000 | 1234.000000 | 1223.000000 |
| mean | 616.500000 | 2013.400324 | 58507.288493 | 5.284546 |
| std | 356.369424 | 3.179700 | 35598.702098 | 0.825622 |
| min | 0.000000 | 1996.000000 | 1000.000000 | 2.000000 |
| 25% | 308.250000 | 2011.000000 | 34000.000000 | 5.000000 |
| 50% | 616.500000 | 2014.000000 | 54572.500000 | 5.000000 |
| 75% | 924.750000 | 2016.000000 | 75000.000000 | 5.000000 |
| max | 1233.000000 | 2019.000000 | 350000.000000 | 10.000000 |
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 2 Engine 36 Power 36 Seats 42 New_Price 5195 Price 0 dtype: int64
Missing values in first list: {'Volkswagen CrossPolo 1.2 TDI', 'Mahindra KUV 100 mFALCON D75 K6 5str AW', 'Mercedes-Benz E-Class E240 V6 AT', 'Mahindra Scorpio VLS 2.2 mHawk', 'Honda Accord 2001-2003 2.3 VTI L MT', 'Skoda Superb Petrol Ambition', 'Skoda Laura 1.8 TSI Ambition', 'Land Rover Freelander 2 S Business Edition', 'Honda Civic 2010-2013 1.8 V AT', 'Skoda Rapid Ultima 1.6 TDI Ambition Plus', 'Hyundai Santro LS zipDrive Euro I', 'Toyota Land Cruiser Prado VX L', 'Hyundai Creta 1.6 VTVT Base', 'Renault Lodgy 110PS RxL', 'Hyundai i20 1.4 Asta AT (O) with Sunroof', 'Volkswagen Vento 1.5 TDI Highline Plus', 'Maruti Swift VVT ZXI', 'Mahindra KUV 100 D75 K8 5Str', 'Hindustan Motors Contessa 2.0 DSL', 'Nissan Terrano XE 85 PS', 'Hyundai Verna Transform SX VGT CRDi BS III', 'Volkswagen Jetta 2007-2011 1.6 Trendline', 'Hyundai EON 1.0 Era Plus', 'Toyota Corolla Altis GL', 'Hyundai Elantra GT', 'Mercedes-Benz S Class 2005 2013 320 L', 'Audi Q5 2008-2012 3.0 TDI Quattro', 'Maruti 800 DX', 'Fiat Punto 1.4 Emotion', 'Honda Mobilio V i VTEC', 'Mahindra Bolero SLX', 'Jaguar XF 2.0 Petrol Portfolio', 'Tata Tiago 1.05 Revotorq XT Option', 'BMW 5 Series 520d Sedan', 'Hyundai Accent GLX', 'Honda Amaze VX CVT i-VTEC', 'Chevrolet Enjoy Petrol LTZ 7 Seater', 'Ford Freestyle Titanium Plus Diesel', 'Nissan 370Z AT', 'Tata Indica Vista Aqua 1.2 Safire', 'Mitsubishi Pajero Sport 4X2 AT', 'Mahindra KUV 100 mFALCON G80 K4 5str', 'Mercedes-Benz A Class Edition 1', 'Tata Sumo EX 10/7 Str BSII', 'Mahindra Xylo E9', 'Maruti Vitara Brezza ZDi AMT', 'Chevrolet Spark 1.0 PS', 'Honda City i DTec VX Option BL', 'Toyota Etios Cross 1.2L G', 'Volkswagen Vento 1.5 TDI Highline Plus AT', 'OpelCorsa 1.4Gsi', 'Chevrolet Enjoy 1.4 LTZ 8', 'Maruti Ciaz VDi Option SHVS', 'Maruti Vitara Brezza ZDi Plus AMT', 'Mercedes-Benz E-Class 250 D W 124', 'Mahindra Bolero Power Plus ZLX', 'Tata Tiago AMT 1.2 Revotron XTA', 'Hyundai i20 2015-2017 Magna Optional 1.4 CRDi', 'Maruti Ertiga VXI Petrol', 'Honda BR-V i-DTEC S MT', 'Honda BR-V i-VTEC VX MT', 'Mahindra Scorpio VLX 2WD BSIII', 'Tata Indica V2 DiCOR DLG BS-III', 'Ford Ikon 1.4 ZXi', 'Volvo S60 D5 Kinetic', 'Hyundai Verna Transform VTVT with Audio', 'Chevrolet Sail Hatchback 1.2', 'Toyota Etios Liva VD', 'Toyota Innova 2.0 V', 'Fiat Avventura FIRE Dynamic', 'Maruti Swift AMT ZXI', 'Honda CR-V Diesel', 'BMW 7 Series 740i Sedan', 'Fiat Abarth 595 Competizione', 'Honda Jazz VX CVT', 'Ford Classic 1.4 Duratorq CLXI', 'Skoda Laura L and K MT', 'Maruti Swift 1.3 VXi', 'Tata Indica Vista Aqua TDI BSIII', 'Ford Fiesta Classic 1.6 Duratec LXI', 'Land Rover Discovery 4 TDV6 Auto Diesel', 'Maruti Ignis 1.2 AMT Delta', 'Toyota Innova 2.5 GX 8 STR', 'Chevrolet Enjoy 1.3 TCDi LTZ 7', 'Hyundai EON 1.0 Kappa Magna Plus', 'Hyundai Creta 1.6 SX Diesel', 'Skoda Octavia 2.0 TDI MT Style', 'Fiat Punto EVO 1.3 Emotion', 'Maruti Ciaz VXi', 'Hyundai Elantra SX AT', 'Fiat Linea Classic 1.3 Multijet', 'Hyundai i20 new Sportz AT 1.4', 'Bentley Flying Spur W12', 'BMW 3 Series GT 320d Sport Line', 'Skoda Laura 1.9 TDI MT Elegance', 'Tata Tigor 1.2 Revotron XZ Option', 'Honda City ZX VTEC Plus', 'Mercedes-Benz GLA Class 220 d 4MATIC', 'Tata Indica Vista Quadrajet LX', 'Maruti SX4 ZXI AT', 'Maruti Alto XCITE', 'Tata Indica Vista Terra 1.2 Safire BS IV', 'Hyundai Verna 1.4 CX', 'Mahindra TUV 300 2015-2019 T8 AMT', 'Mahindra Scorpio S10 8 Seater', 'Hyundai Xcent 1.2 CRDi SX', 'Mahindra Thar 4X4', 'Mahindra Scorpio VLX Special Edition BS-IV', 'Renault Pulse RxZ', 'Hyundai Santro Xing XG AT eRLX Euro III', 'Land Rover Discovery 4 SDV6 SE', 'Maruti Wagon R VXI AMT Opt', 'Maruti Ritz VDi ABS', 'Hyundai Tucson 2.0 e-VGT 4WD AT GLS', 'Mahindra Xylo H9', 'Ford Endeavour 3.0L AT 4x2', 'Toyota Innova Crysta Touring Sport 2.4 MT', 'Nissan Teana XL', 'Honda Jazz 2020 Petrol', 'Hyundai Accent Executive LPG', 'Nissan Micra XL CVT', 'Toyota Camry MT with Moonroof', 'Tata Indica Vista Terra Quadrajet 1.3L BS IV', 'Mercedes-Benz CLA 45 AMG', 'Toyota Etios Liva Diesel TRD Sportivo', 'Mercedes-Benz B Class B180 Sports', 'BMW X3 2.5si', 'Isuzu MU 7 4x2 HIPACK', 'Toyota Etios Liva 1.4 VXD', 'Volkswagen Vento 1.6 Trendline', 'Hyundai Sonata Embera 2.4L MT', 'Honda BRV i-DTEC V MT', 'Chevrolet Enjoy TCDi LS 7 Seater', 'Hyundai Elite i20 Magna Plus', 'Ford EcoSport 1.5 Petrol Ambiente', 'Renault Koleos 4X2 MT', 'Fiat Linea Dynamic', 'Datsun GO T Petrol', 'Volkswagen Polo ALLSTAR 1.2 MPI', 'Mahindra Verito Vibe 1.5 dCi D6', 'Volkswagen Vento 1.2 TSI Comfortline AT', 'Mahindra Scorpio SLX 2.6 Turbo 8 Str', 'Tata Manza Club Class Safire90 LX', 'Maruti A-Star Zxi', 'Mahindra TUV 300 P4', 'Hyundai Creta 1.6 SX Automatic', 'BMW 5 Series 530i Sport Line', 'Ford Fiesta Classic 1.6 SXI Duratec', 'Land Rover Range Rover HSE', 'Mahindra KUV 100 mFALCON D75 K2', 'Toyota Innova 2.5 LE 2014 Diesel 8 Seater', 'Fiat Avventura Urban Cross 1.3 Multijet Emotion', 'Hyundai i20 Active SX Diesel', 'Maruti Celerio X VXI Option', 'Fiat Grande Punto 1.2 Emotion', 'Maruti Versa DX2', 'Hyundai Santro Xing GLS CNG', 'Renault Duster 85PS Diesel RxZ', 'Honda Amaze E i-DTEC', 'Jeep Compass 1.4 Sport', 'Audi Q3 30 TDI S Edition', 'Mercedes-Benz B Class B180 Sport', 'Mahindra KUV 100 G80 K4 Plus 5Str', 'Ford Fiesta 1.4 SXI Duratorq', 'Hyundai i20 2015-2017 1.4 CRDi Sportz', 'Honda WRV i-DTEC VX', 'Honda Civic 2010-2013 1.8 S MT Inspire', 'BMW 7 Series 730Ld DPE Signature'}
False
Missing values in first list: {'Isuzu MU', 'Nissan 370Z', 'Bentley Flying', 'Toyota Land', 'Hindustan Motors', 'Fiat Abarth', 'OpelCorsa 1.4Gsi'}
Missing values in first list: set()
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Price 0 Cars 0 dtype: int64
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Price 0 Cars 0 dtype: int64
Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Cars 0 dtype: int64
Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage float64 Engine float64 Power float64 Seats float64 Cars object dtype: object
Data Analysis¶
Price 1.000000 Power 0.769351 Engine 0.659117 Year 0.305800 Seats 0.052262 Kilometers_Driven -0.011263 Mileage -0.313877 Name: Price, dtype: float64
Conclusion- According to the stats for choosing a necessary and optimum used car, a customer will prefer price at the first place and mileage at the last accordingly. An average typical second hand car customer prefers decent price at its first place.
[<matplotlib.lines.Line2D at 0x208cc500e80>]
Conclusion- Converted the value of Price to Log(Price) for a good solution to have a more normal visualization of the distribution of the Price.
Conclusion- The above pie chart indicates the price of particular fuel engines(diesel, petrol, CNG, LPG) Also it indicates that the market price of diesel engines is more as compared to other fuel type engines. Also diesel users are greater in market compared to others as it gives better mileage.
Conclusion- According to the plot, the customers using automatic transmission mode vehicles are increasing rapidly in consecutive years.
Conclusion- According to the plot, the customers using diesel driven vehicles are increasing rapidly in consecutive years.
Conclusion- From graph it is clear that in CNG and LPG driven cars only manual mode of transmission is available whereas automatic mode of transmission leads in diesel and petrol driven cars(disesel being the most used).
Conclusion- The graph clearly indicates that people prefer Manual mode of Transmission over Automatic one
Model fitting¶
| model | Root Mean Squared Error | Accuracy on Traing set | Accuracy on Testing set | |
|---|---|---|---|---|
| 3 | MLPRegressor | 209.405833 | 0.678774 | 0.634821 |
| 4 | AdaBoostRegressor | 149.27056 | 0.828892 | 0.814443 |
| 0 | DecisionTreeRegressor | 113.561756 | 0.999993 | 0.892603 |
| 2 | RandomForestRegressor | 84.187587 | 0.991894 | 0.940977 |
| 5 | ExtraTreesRegressor | 81.025873 | 0.999993 | 0.945327 |
| 1 | XGBRegressor | 74.815814 | 0.994635 | 0.953386 |
| Car_id | Price | |
|---|---|---|
| 0 | 0 | 153.78 |
| 1 | 1 | 118.42 |
| 2 | 2 | 942.82 |
| 3 | 3 | 155.14 |
| 4 | 4 | 283.97 |
Observation- The above model displays prediction of the car price for respective specifications given in the feature1 array.
Additional Analysis¶
array(['Mumbai', 'Pune', 'Chennai', 'Coimbatore', 'Hyderabad', 'Jaipur',
'Kochi', 'Kolkata', 'Delhi', 'Bangalore', 'Ahmedabad'],
dtype=object)
array(['Maruti Wagon', 'Hyundai Creta', 'Honda Jazz', 'Maruti Ertiga',
'Audi A4', 'Hyundai EON', 'Nissan Micra', 'Toyota Innova',
'Volkswagen Vento', 'Tata Indica', 'Maruti Ciaz', 'Honda City',
'Maruti Swift', 'Land Rover', 'Mitsubishi Pajero', 'Honda Amaze',
'Renault Duster', 'Mercedes-Benz New', 'BMW 3', 'Maruti S',
'Audi A6', 'Hyundai i20', 'Maruti Alto', 'Honda WRV',
'Toyota Corolla', 'Mahindra Ssangyong', 'Maruti Vitara',
'Mahindra KUV', 'Mercedes-Benz M-Class', 'Volkswagen Polo',
'Tata Nano', 'Hyundai Elantra', 'Hyundai Xcent', 'Mahindra Thar',
'Hyundai Grand', 'Renault KWID', 'Hyundai i10', 'Nissan X-Trail',
'Maruti Zen', 'Ford Figo', 'Mercedes-Benz C-Class',
'Porsche Cayenne', 'Mahindra XUV500', 'Nissan Terrano',
'Honda Brio', 'Ford Fiesta', 'Hyundai Santro', 'Tata Zest',
'Maruti Ritz', 'BMW 5', 'Toyota Fortuner', 'Ford Ecosport',
'Hyundai Verna', 'Datsun GO', 'Maruti Omni', 'Toyota Etios',
'Jaguar XF', 'Maruti Eeco', 'Honda Civic', 'Volvo V40',
'Mercedes-Benz B', 'Mahindra Scorpio', 'Honda CR-V',
'Mercedes-Benz SLC', 'BMW 1', 'Chevrolet Beat', 'Skoda Rapid',
'Audi RS5', 'Mercedes-Benz S', 'Skoda Superb', 'BMW X5',
'Mercedes-Benz GLC', 'Mini Countryman', 'Chevrolet Optra',
'Renault Lodgy', 'Mercedes-Benz E-Class', 'Maruti Baleno',
'Skoda Laura', 'Mahindra NuvoSport', 'Skoda Fabia', 'Tata Indigo',
'Audi Q3', 'Skoda Octavia', 'Audi A8', 'Mahindra Verito',
'Mini Cooper', 'Hyundai Santa', 'BMW X1', 'Hyundai Accent',
'Hyundai Tucson', 'Mercedes-Benz GLE', 'Maruti A-Star',
'Fiat Grande', 'BMW X3', 'Ford EcoSport', 'Audi Q7',
'Volkswagen Jetta', 'Mercedes-Benz GLA', 'Maruti Celerio',
'Tata Sumo', 'Honda Accord', 'BMW 6', 'Tata Manza',
'Chevrolet Spark', 'Mini Clubman', 'Nissan Teana', 'Maruti 800',
'Honda BRV', 'Jaguar XE', 'Tata Xenon', 'Audi A3',
'Mercedes-Benz GL-Class', 'Honda BR-V', 'Volvo S80',
'Renault Captur', 'Chevrolet Enjoy', 'Mahindra Bolero', 'Audi Q5',
'Mitsubishi Cedia', 'Maruti S-Cross', 'Skoda Yeti',
'Ford Endeavour', 'Mercedes-Benz GLS', 'Mercedes-Benz A',
'Maruti SX4', 'Toyota Camry', 'Honda Mobilio', 'Fiat Linea',
'Audi TT', 'Mahindra Renault', 'Jeep Compass', 'Ford Ikon',
'Chevrolet Sail', 'Mahindra Quanto', 'Chevrolet Aveo',
'Mahindra Xylo', 'Maruti Esteem', 'Tata Safari', 'Maruti Ignis',
'Jaguar XJ', 'Nissan Sunny', 'Mercedes-Benz SLK-Class',
'Volkswagen Passat', 'Maruti Dzire', 'Chevrolet Cruze',
'Renault Koleos', 'Toyota Qualis', 'Volkswagen Ameo',
'Maruti Grand', 'Datsun redi-GO', 'Smart Fortwo',
'Mitsubishi Outlander', 'Porsche Cayman', 'Mercedes-Benz CLA',
'Volvo XC60', 'Tata New', 'Porsche Boxster', 'Mahindra XUV300',
'Tata Hexa', 'Tata Tiago', 'BMW 7', 'Fiat Avventura', 'Tata Tigor',
'Volvo S60', 'Ambassador Classic', 'Volkswagen Beetle',
'Fiat Petra', 'Hyundai Getz', 'Audi A7', 'Hyundai Elite',
'Ford Aspire', 'Volkswagen Tiguan', 'Chevrolet Captiva',
'Fiat Punto', 'Mahindra TUV', 'BMW X6', 'Tata Bolt',
'Nissan Evalia', 'Renault Scala', 'Mahindra Jeep',
'Hyundai Sonata', 'Ford Freestyle', 'Mahindra Logan',
'Chevrolet Tavera', 'Volvo XC90', 'Renault Pulse',
'Mitsubishi Montero', 'Porsche Panamera', 'Volkswagen CrossPolo',
'Renault Fluence', 'Tata Venture', 'Tata Nexon', 'Isuzu MUX',
'Toyota Platinum', 'Mercedes-Benz R-Class',
'Mercedes-Benz CLS-Class', 'ISUZU D-MAX', 'Mercedes-Benz S-Class',
'Mitsubishi Lancer', 'Ford Classic', 'Datsun Redi', 'Ford Mustang',
'Ford Fusion', 'Fiat Siena', 'Maruti 1000',
'Mercedes-Benz SL-Class', 'BMW Z4', 'Force One', 'Maruti Versa',
'Honda WR-V', 'Bentley Continental', 'Lamborghini Gallardo',
'Jaguar F'], dtype=object)
Location Ahmedabad 223 Bangalore 353 Chennai 490 Coimbatore 634 Delhi 549 Hyderabad 741 Jaipur 410 Kochi 648 Kolkata 530 Mumbai 784 Pune 613 Name: Cars, dtype: int64
[<matplotlib.lines.Line2D at 0x208cd94c940>]
Observation- From above data, we can observe that Mumbai and Hyderabad has maximum number of second hand car users which is our target audience.
Location Ahmedabad Volvo XC60 Bangalore Volvo V40 Chennai Volvo S80 Coimbatore Volvo S60 Delhi Volvo S60 Hyderabad Volvo XC60 Jaipur Volkswagen Vento Kochi Volvo XC90 Kolkata Volkswagen Vento Mumbai Volvo S60 Pune Volvo XC60 Name: Cars, dtype: object
Conclusion- The above data provides information that the respective corresponding car models is of the highest demand in that particular city. From this result we can conclude that since Hyderabad and Mumbai have the highest number of second hand car users thus the availability of their respective cars ie. Volvo XC60 and Volvo S60 respectively are the target cars with maximum number of selling units. Similarly, the selling units of the corresponding cars is more in their respective locations. Hence it can be the our main selling main point.
Conclusion - This is a powerbi report comparing various columns of the data provided giving clear analysis of relation among themselves.
Prediction by User Input¶
Enter your own data to test the model:
There was an error when executing cell [54]. Please run Voilà with --show_tracebacks=True or --debug to see the error message, or configure VoilaConfiguration.show_tracebacks.